Direction Of Arrival Estimation Research Articles

Trilinear decomposition based near-field source localization with MIMO velocity vector sensor arrays

This paper studies the near-field (NF) parameter estimation problem for a multiple-input multiple-output (MIMO) array system, which employs multiple pairs of orthogonal velocity sensors at both the transmitter and the receiver. A trilinear decomposition method is proposed to estimate the four-dimensional (4-D) parameters, including the direction of departure (DOD), the range from transmitter to target (RFTT), the direction of arrival (DOA), and the range from target to receiver (RFTR). Firstly, the output of the matched filter at the receiver is formulated in a third-order parallel factor (PARAFAC) model; secondly, the initial coarse estimates of DOD, RFTT, DOA and RFTR embedded in the velocity vector sensors are obtained through trilinear decomposition, and then more accurate estimates of DOD, RFTT, DOA and RFTR are achieved from the steering vector. The proposed method is search-free and has a close form, with automatically paired results. Its performance is demonstrated via numerical examples.

Improved block sparse Bayesian learning based DOA estimation for massive MIMO systems

Direction-of-arrival (DOA) estimation is crucial for suppressing inter-cell interference in massive multiple-input multiple-output (MIMO) systems. However, conventional algorithms are unsuitable for low signal-to-noise ratio (SNR) and a small number of snapshots. Consequently, in an attempt to achieve fast reconstruction speed while ensuring high accuracy, this study proposes an improved block sparse Bayesian learning-based DOA estimation algorithm for two-dimension massive MIMO systems. Using the sparse characteristics and inherent structure of the signal, the DOA estimation problem is transformed to a joint task of sparse signal reconstruction and parameter optimization. Subsequently, the estimation accuracy is improved through complete excavation and learning of the signal temporal structure. In addition, to avoid the direct solution of the posterior probability distribution, reduce the number of iterations and increase the reconstruction speed, variational inference is used to approximate the Bayesian model and solve the joint problem through parameter learning. Moreover, the auxiliary angle is introduced to avoid extra angle matching, which further reduces the complexity. Simulations are performed to verify the proposed method and the results prove its superiority when applied to massive MIMO systems compared with some existing DOA estimation algorithms.

A Joint Angle and Frequency Spectrum Estimation Algorithm Using Difference Coarray

The spectrum sensing that jointly estimates direction-of-arrival (DOA) and frequency spectrum is an important issue for cognitive radio. Existing off-grid DOA estimation algorithms using difference coarray require a large number of snapshots to guarantee the estimation accuracy. Meanwhile, inaccurate DOA estimation renders inaccurate frequency spectrum estimation due to coupled estimation process. In order to overcome these disadvantages, we propose a joint angle and frequency spectrum estimation algorithm using difference coarray in this work. The proposed algorithm first transforms the received signal into the coarray domain and then adopts an “estimate and subtract” method to separate multiple signals. Subsequently, the DOA estimate of each source is corrected iteratively based on the proposed simple interpolation method. Finally, the frequency spectra are obtained by estimating the frequency response matrix based on the previous estimated DOAs. The shortage of requiring a large number of snapshots are overcome by utilizing the deterministically orthogonal signal model in which the signals are completely uncorrelated. Simulation results demonstrate the effectiveness of the proposed algorithm for off-grid DOA and frequency spectrum estimation using difference coarray with few snapshots.

IoMT-Net: Blockchain-Integrated Unauthorized UAV Localization Using Lightweight Convolution Neural Network for Internet of Military Things

Unmanned aerial vehicle (UAV) contributes substantial strategic benefits on the Internet-of-military-things (IoMT). However, the untrusted party’s misuse of the UAV may violate the security and even demolish the critical operation in the IoMT system. In addition, data manipulation and falsification using unauthorized access are the significant challenges of the IoMT system. In response to this problem, this study proposes a blockchain integrated convolution neural network (CNN)-based intelligent framework named IoMT-Net for identification and tracking illegal UAV in the IoMT system. Blockchain technology prevents illicit access, data manipulation, and illegal intrusions, as well as stored data on the central control server (CCS). Concurrently, the proposed CNN analyzed the radio frequency signal sent by the antenna array element to determine the direction of arrival (DoA) for the localization of the illegal UAV. Therefore, a signal model is designed to processes the received signal array through IoMT-Net. Moreover, the proposed CNN model is designed with two different functional modules, such as the resource accuracy trade-off (RAT) module and the unique feature extraction and accuracy boosting (UAB) module, by adopting depth-wise and grouped convolution layers. These sparsely connected convolution layers offer high DoA estimation accuracy while maintaining the network more lightweight. In addition, the skip connection is also leveraged into the sub-units of RAT and UAB modules for sharing features and handling the vanishing gradients problem. Based on the simulation results, the proposed network achieves superior DoA estimation accuracy (approximately 97.63% accuracy at 10 dB SNR) and outperforms other state-of-the-art models.

Combined noise reduction and DOA estimation algorithm for MEMS vector hydrophone based on variational mode decomposition

PurposeThis paper aims to solve the problem that strong noise interference seriously affects the direction of arrival (DOA) estimation in complex underwater acoustic environment. In this paper, a combined noise reduction algorithm and micro-electro-mechanical system (MEMS) vector hydrophone DOA estimation algorithm based on singular value decomposition (SVD), variational mode decomposition (VMD) and wavelet threshold denoising (WTD) is proposed.Design/methodology/approachFirstly, the parameters of VMD are determined by SVD, and the VMD method can decompose the signal into multiple intrinsic mode functions (IMFs). Secondly, the effective IMF component is determined according to the correlation coefficient criterion and the IMF less than the threshold is processed by WTD. Then, reconstruction is carried out to achieve the purpose of denoising and calibration baseline drift. Finally, DOA estimation is achieved by the combined directional algorithm of preprocessed signal.FindingsSimulation and field experiments results show that the algorithm has good noise reduction and baseline drift correction effects for nonstationary underwater signals, and high-precision azimuth estimation is realized.Originality/valueThis research provides the basis for MEMS hydrophone detection and positioning and has great engineering significance in underwater detection system.

Off-Grid DOA Estimation Based on Compressed Sensing on Multipath Environment

Due to the existence of multipath propagation, the array will receive the multipath propagation signals at the same time while receiving the target signal and the performance of directional of arrival (DOA) estimation will be influenced. In this paper, an off-grid DOA estimation technique based on compressed sensing (CS) on multipath environment is proposed. To deal with the off-grid problem and the multipath propagation problem, we present a compressed sensing based method. This method regards the multipath propagation coefficients vector as a superparameter and the grid as an adjustable parameter. Then, the problem of multipath propagation coefficients estimation is converted to the estimation of an error matrix and the grid is refined iteratively. The simulation results show that the method can achieve off-grid DOA estimation in the presence of the multipath propagation signals.

Performance Improvement of Bistatic Baseline Detection

For bistatic detection, the desired target is always smeared by the direct blast, limited by the performance of direction-of-arrival (DOA) estimation algorithms. Thus, a deconvolved Multiple-Signal-Classification (D-MUSIC) DOA estimation algorithm is proposed. D-MUSIC beam power is obtained by deconvolving MUSIC beam power using the point-scattering-function (PSF) and Richardson-Lucy (R-L) iteration algorithm. Under low signal-to-noise ratios (SNRs), the background level of D-MUSIC beam power is much lower than that of MUSIC beam power. Besides, D-MUSIC can realize the weak target detection with an adjacent strong interferer in the mainlobe. Finally, via processing the actual data collected during the Sanya sea trial, we analyze the bearing time record (BTR) of MUSIC and D-MUSIC, verifying the superiority of D-MUSIC algorithm.

Multitarget 2-D DOA Estimation Using Wideband LFMCW Signal and Triangle Array Composed of Three Receiver Antennas

Direction of arrival (DOA) estimation has been a primary focus of research for many years. Research on DOA estimation continues to be immensely popular in the fields of the internet of things, radar, and smart driving. In this paper, a simple new two-dimensional DOA framework is proposed in which a triangular array is used to receive wideband linear frequency modulated continuous wave signals. The mixed echo signals from various targets are separated into a series of single-tone signals. The unwrapping algorithm is applied to the phase difference function of the single-tone signals. By using the least-squares method to fit the unwrapped phase difference function, the DOA information of each target is obtained. Theoretical analysis and simulation demonstrate that the framework has the following advantages. Unlike traditional phase goniometry, the framework can resolve the trade-off between antenna spacing and goniometric accuracy. The number of detected targets is not limited by the number of antennas. Moreover, the framework can obtain highly accurate DOA estimation results.

Impact of antenna directivity on compressed sensing-based DOA estimation employing UCA antenna

This letter investigates the impact of the antenna element directivity on compressed sensing (CS)-based direction of arrival (DOA) estimation employing the uniform circular array (UCA) antenna. In the CS-based approach, the reconstruction accuracy of sparse signals is determined by a sensing matrix, which depends on the array antenna configuration in the DOA estimation. In our study, we analyze the cross-correlation between column vectors of the sensing matrix and clarify the estimation accuracy for various antenna element radiation patterns.

Deep Neural Networks for Direction of Arrival Estimation of Multiple Targets With Sparse Prior for Line-of-Sight Scenarios

Received signal Direction of Arrival (DOA) estimation represents a significant problem with multiple applications, ranging from wireless communications to radars. This problem presents significant challenges, mainly given by a large number of closely located transmitters being difficultly separable. Currently available state of the art approaches fail in providing sufficient resolution to separate and recognize the DOA of closely located transmitters, unless using a large number of antennas and hence increasing the deployment and operation costs. In this paper, we present a deep learning framework for DOA estimation under Line-of-Sight scenarios, which able to distinguish a number of closely located sources higher than the number of receivers' antennas. We first propose a formulation that maps the received signal to a higher dimensional space that allows for better identification of signal sources. Secondly, we introduce a Deep Neural Network that learns the mapping from the receiver antenna space to the extended space to avoid relying on specific receiver antenna array structures. Thanks to our approach, we reduce the hardware complexity compared to state of the art solutions and allow reconfigurability of the receiver channels. Via extensive numerical simulations, we demonstrate the superiority of our proposed method compared to state-of-the-art deep learning-based DOA estimation methods, especially in demanding scenarios with low Signal-to-Noise Ratio and limited number of snapshots.

Enhanced DOA Estimation With Augmented CADiS by Exploiting Array Motion Strategies

With its increased aperture and enlarged minimum spacing among sensors, coprime array with displaced subarrays (CADiS) receives a great deal of attention among sparse arrays. However, the holes in the difference co-array of CADiS extremely degrade the achievable number of uniform degrees of freedom (uDOFs). In recent years, increasing researches of direction of arrival (DOA) estimation with moving sparse arrays have brought new insights into the releasing the holes problem. In this paper, we first explore the structure of difference co-array generated from array motion, and propose a hole-free motion strategy that can fill all the holes in difference co-array of coprime arrays, and resulting increased number of uDOFs as well as array aperture. Secondly, based on the analysis of hole positions, we propose another motion strategy that needs only one step motion of CADiS to generate a difference co-array with no holes in the central part. We find all the qualified steps length of the one-step motion method and provide the closed-form expression of the corresponding achievable number of uDOFs. Finally, the number of uDOF and DOA estimation accuracy of the proposed motion strategies are compared with several state-of-the-art motion methods. Numerical results show the superiority of the proposed methods.

DOA Estimation for Passive Radar Under the Scene Where Target Echo Is Partially Correlated

In this paper we consider that there is a position difference between the reference antenna and surveillance array in a passive radar system. In this scenario, the target echo in the reference channel and surveillance array is partially correlated instead of completely correlated. Due to the correlation of target echo, adaptive clutter cancellation will influence the target steering vector and cause direction of arrival (DOA) estimation bias. To solve this problem, we establish the array signal model where the target signal in the reference channel and surveillance channels is partially correlated and analyze the influence of the adaptive clutter cancellation on the target steering vector. Then, a DOA estimation method suitable for this scenario is proposed to correct the DOA bias. Simulations and real radar data show that the proposed method can correct DOA bias in this scene, thereby improving the location accuracy of passive radar.

Full-duplex underwater acoustic communications via self-interference cancellation in space

Traditionally, underwater acoustic communications are half-duplex (HD), i.e., the hydrophones and transducers operate in non-overlapping time-slots/frequency-bands in one direction. To double the spectral efficiency into the full-duplex (FD) mode, which transmit and receive signals simultaneously, a self-interference cancellation (SIC) technique in space is introduced and deployed. Specifically, a novel underwater acoustic system is proposed to perform FD-SIC efficiently via an integrated design combining underwater acoustic vector sensor (AVS) and phased array transducer (PAT) to realize the spatial SIC. The energy focusing function of the beamformer (BF) helps PAT avoid self and mutual spatial interference. The AVS keeps updating the direction of arrival information to let BF adjust the steering angle via an adaptive protocol. The field-programmable gate array (FPGA) Direction of Arrival Estimation (DOA) estimation is done by a 12-element AVS, run-time is 1.22×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−4</sup> s by MUSIC method, which is promising for real-time applications. In this work, phase-shift keying (PSK) and PSK-based orthogonal frequency-division multiplexing (OFDM) are deployed as the FD transmission modulation methods in the spacial SIC. The proposal is evaluated and verified via both simulations and emulations in real underwater acoustic channels collected in the Gulf of La Spezia, Italy, and it is able to achieve 59 dB SIC while modulating with binary phase-shift keying (BPSK) with carrier wave frequency 80 kHz and AVS steering angle −5°. Moreover, it has at least 37 dB SIC within the steering angle region.

DOA and subarray-interval estimation for arbitrarily distributed UAV swarm system

In this paper, DOA and subarray-interval estimation are considered and applied to arbitrarily distributed unmanned aerial vehicle (UAV) swarm system, in which multiple small UAVs containing uniform linear array (ULA) are divided by unknown intervals because of dynamic moving. Three parameters are taken to indicate the steering vector, namely, the direction of arrivals (DOAs) of target users, the intervals of UAVs, and the orientation angles of UAVs. The orientation angles are first estimated with an auxiliary user and the DOAs are obtained through a search free rooting method, despite the intervals among the UAVs. Afterwards, the intervals among UAVs can also be calculated via exhaustive when the number of target users are no less than three. We further develop a low-complex method to reduce the computational complexity during subarray-interval estimation. The deterministic Cramer-Rao bound (CRB) of the DOA, orientation angle and subarray-interval can be inferred in a closed form. Eventually, numerical instances are cited to verify the research results.

Passive synthetic aperture for direction-of-arrival estimation using sparse Bayesian learning

Passive synthetic aperture (PSA) extension for a moving array has the ability to enhance the accuracy of direction-of-arrival (DOA) estimation by constructing a larger virtual aperture. The array element overlap in array continuous measurements is required for the traditional extended towed array measurement (ETAM) methods. Otherwise, the phase factor estimation is biased, and the aperture extension fails when multiple sources exist. To solve this problem, passive aperture extension with sparse Bayesian learning (SBL) is proposed. In this method, SBL is used to simultaneously estimate the phase correction factors of different targets, followed by phase compensation applied to the extended aperture manifold vectors for DOA estimation. Simulation and experimental data results demonstrate that this proposed method successfully extends the aperture and provides higher azimuth resolution and accuracy compared to conventional beamforming (CBF) and SBL without extension. Compared with the traditional ETAM methods, the proposed method still performs well even when the array elements are not overlapped during the motion.

Алгоритм оценки углов прихода сигналов в системе распределенных некогерентных автомобильных радаров

The present paper is focused on processing of signals from multiple sensors in non-coherent mode that provide improvement of angular resolution of close targets compared to single radar. The low-complexity technique of Direction-of-Arrival (DoA) estimation in Automotive Distributed Non-Coherent Multi-Radar Systems in the case of short length of snapshots is proposed. This technique consists of three main stages: beam-scanning procedure, transformation of the received signals of two radars to the unified coordinate system, and joint DoA estimation algorithm. The DoA method is represented by two approaches: (i) the Capon method modified for joint signal processing; (ii) low-complexity algorithm based on theory of auto-compensator. Performance of the system is investigated on the base of Monte-Carlo simulation. Current algorithm shows improvement of system performance of distributed non-coherent multi-radar systems compared to single radar.

Sparse Bayesian learning for sparse signal recovery using [formula omitted]-norm

Sparse signal recovery is an important technique for acoustic direction-of-arrival (DOA) estimation which is widely used in applications such as humanoid robots and voice control loudspeakers. However, the estimation accuracy performance is limited by the size of platform and the sound wavelength. To improve the accuracy performance, we propose a sparse signal recovery approach for acoustic DOA estimation based on a hierarchical formulation of the generalized Gaussian prior with a shape parameter q=1/2. Specifically, a sparse Bayesian learning (SBL) framework is built using ℓ1/2-norm priors to efficiently utilize the space sparsity nature of acoustic sources. The priors, built primarily based on a hierarchical structure with a couple of layers, are used for modeling the sparse signals. Then, an expectation–maximization algorithm is proposed for inference and parameter learning. The estimation accuracy performance of the proposed approach is verified in terms of sparse signal recovery and acoustic DOA estimation. The experimental results exhibit that the proposed method achieves the highest recovery accuracy performance and has the lowest root mean square error (RMSE) in contrast with the state-of-the-art sparse signal recovery methods.

An Efficient 2D DOA Estimation Algorithm Based on OMP for Rectangular Array

Recently, orthogonal matching pursuit (OMP) has been widely used in direction of arrival (DOA) studies, which not only greatly improves the resolution of DOA, but can also be applied to single-snapshot and coherent source cases. When applying the OMP algorithm to the rectangular array DOA of the millimeter-wave radar, it is necessary to reshape the two-dimensional (2D) signal into a long one-dimensional (1D) signal. However, the long 1D signal will greatly increase the number and length of atoms in the complete dictionary of the OMP algorithm, which will greatly increase the amount of computation. Taking advantage of the sparsity of targets in the DOA space, an efficient 2D DOA estimation algorithm based on OMP for rectangular array is proposed. The main idea is to reduce the number of atoms in the complete dictionary of the OMP algorithm, thereby greatly reducing the amount of computation required. A simulation verifies that the efficiency of the proposed algorithm is much higher than the conventional algorithm with almost the same estimation accuracy.

Direction finding by sparse‐based approach for dual‐polarised conformal array in the presence of gain and phase uncertainties

AbstractThis article investigates the problem of robust direction‐of‐arrival (DOA) estimation with unknown sensor gain and phase uncertainties. A Dual‐Polarised Regularised Multiple FOCal Underdetermined System Solver is proposed to estimate the 2‐D DOA for the dual‐polarised conformal array. Each polarisation signal is decomposed into two orthogonal components, which are regarded as a pair of coherent signals, and the polarisation parameters are merged into the signal waveform. A sparse‐based method based on the coherent signals block is presented, implementing weight sharing and grid pruning within the block to alleviate the negative influence of the errors. Meanwhile, gain and phase errors are modelled as the equivalent noise, which can be suppressed with the adaptive adjustment for the regularisation parameter. As the insensitivity of the sparse‐based algorithm to coherent signals, the proposed algorithm can realise the blind polarisation 2‐D DOA estimation. The Cramer–Rao bound is derived, and simulations exhibit the effectiveness and robustness of the proposed method with the unknown sensor gain and phase uncertainties.

Improving the beamforming performance of a vector sensor line array with a coprime array configuration

Coprime arrays provide appealing performance in many scenarios. In this paper, we show the benefits of spatially undersampled acoustic vector hydrophone coprime line arrays over traditional uniform hydrophone line arrays for beamforming and direction-of-arrival (DOA) estimation. This paper presents the perceptiveness of the theoretical performance of an underwater vector sensor coprime line array (vCLA) in the physical array domain. Based on the Product and Min methods, an array extending approach for the vCLA has been proposed to reduce the area of grating lobes of beam output leading to unambiguous DOA estimates. To minimize the sidelobe and improve the left–right suppression ability of the array, we present a data-independent weighting design method based on sound pressure and vibration velocity joint processing (PVJP) and evaluate its performance in detail. The results suggest that the proposed method can improve the vCLA beam output directivity, enhance the spatial resolution, benefit the left–right ambiguity rejection capability, and productively mitigate the influence of side and grating lobes, thus promoting the DOA estimation accuracy.